Slow epidemic extinction in populations with heterogeneous infection rates

We explore how heterogeneity in the intensity of interactions between people affects epidemic spreading. For that, we study the susceptible-infected-susceptible model on a complex network, where a link connecting individuals $i$ and $j$ is endowed with an infection rate $\beta_{ij} = \lambda w_{ij}$ proportional to the intensity of their contact $w_{ij}$, with a distribution $P(w_{ij})$ taken from face-to-face experiments analyzed in Cattuto $et\;al.$ (PLoS ONE 5, e11596, 2010). We find an extremely slow decay of the fraction of infected individuals, for a wide range of the control parameter $\lambda$. Using a distribution of width $a$ we identify two large regions in the $a-\lambda$ space with anomalous behaviors, which are reminiscent of rare region effects (Griffiths phases) found in models with quenched disorder. We show that the slow approach to extinction is caused by isolated small groups of highly interacting individuals, which keep epidemic alive for very long times. A mean-field approximation and a percolation approach capture with very good accuracy the absorbing-active transition line for weak (small $a$) and strong (large $a$) disorder, respectively

Effect of degree correlations above the first shell on the percolation transition

The use of degree-degree correlations to model realistic networks which are characterized by their Pearson's coefficient, has become widespread. However the effect on how different correlation algorithms produce different results on processes on top of them, has not yet been discussed. In this letter, using different correlation algorithms to generate assortative networks, we show that for very assortative networks the behavior of the main observables in percolation processes depends on the algorithm used to build the network. The different alghoritms used here introduce different inner structures that are missed in Pearson's coefficient. We explain the different behaviors through a generalization of Pearson's coefficient that allows to study the correlations at chemical distances l from a root node. We apply our findings to real networks.Comment: In press EP

When orofacial pain needs a heart repair

Objectives: The association of chronic orofacial pain (COFP) and congenital heart disease has never previously been reported. We report the first case of COFP secondary to a right-to-left shunt (RLS) due to asymptomatic patent foramen ovale (PFO) in a patient with prothrombotic states. Materials and methods: A 48-year-old female patient presented with a 10-month history of left-sided facial pain who was initially diagnosed with persistent idiopathic facial pain (PIFP) on account of its similar characteristics. Magnetic resonance imaging (MRI) of the brain revealed gliosis and carotid siphon tortuosity; in addition, hyperhomocysteinaemia due to the homozygosis mutation for 5,10 MethyleneTetraHydroFolate Reductase was identified. Transcranial doppler ultrasonography was requested from a neurology consultant which revealed a high degree of RLS. Subsequently, a cardiological evaluation was performed; the specialist requested a transesophageal echocardiography that detected an interatrial septum aneurysm with PFO. Results: Based on the analysis of the patient's high degree of RLS, prothrombotic state and gliosis in relation to age, the cardiological consultant chose to perform a percutaneous closure of the PFO to avoid the risk of a cryptogenic stroke. After PFO closure, a complete remission of the pain was obtained. Conclusions: The disappearance of the pain supports the possible association between RLS and COFP. PFO with RLS has been suggested as a risk factor for cryptogenic stroke, especially in association with other thromboembolic risk factors. Therefore, the early detection, in this case, could be considered a possible lifesaver. Communication between different care providers is essential when the patient presents symptoms of facial pain which are of an atypical nature

Numerical Study of the Optical Response of ITO-In2O3 Core-Shell Nanocrystals for Multispectral Electromagnetic Shielding

Nowadays materials to protect equipment from unwanted multispectral electromagnetic waves are needed in a broad range of applications including electronics, medical, military and aerospace. However, the shielding materials currently in use are bulky and work effectively only in a limited frequency range. Therefore, nanostructured materials are under investigation by the relevant scientific community. In this framework, the design of multispectral shielding nanomaterials must be supplemented with proper numerical models that allow dealing with non-linearities and being effective in predicting their absorption spectra. In this study, the electromagnetic response of metal-oxide nanocrystals with multispectral electromagnetic shielding capability has been investigated. A numerical framework was developed to predict energy bands and electron density profiles of a core-shell nanocrystal and to evaluate its optical response at different wavelengths. To this aim, a finite element method software is used to solve a non-linear Poisson's equation. The numerical simulations allowed to model the optical response of ITO-In2O3 core-shell nanocrystals and can be effectively applied to different nanotopologies to support an enhanced design of nanomaterials with multispectral shielding capabilities

Sparse Exploratory Factor Analysis

Sparse principal component analysis is a very active research area in the last decade. It produces component loadings with many zero entries which facilitates their interpretation and helps avoid redundant variables. The classic factor analysis is another popular dimension reduction technique which shares similar interpretation problems and could greatly benefit from sparse solutions. Unfortunately, there are very few works considering sparse versions of the classic factor analysis. Our goal is to contribute further in this direction. We revisit the most popular procedures for exploratory factor analysis, maximum likelihood and least squares. Sparse factor loadings are obtained for them by, first, adopting a special reparameterization and, second, by introducing additional [Formula: see text]-norm penalties into the standard factor analysis problems. As a result, we propose sparse versions of the major factor analysis procedures. We illustrate the developed algorithms on well-known psychometric problems. Our sparse solutions are critically compared to ones obtained by other existing methods

Comparing comparators: A look at control arms in kidney cancer studies over the years

In the past decade, an increasing number of frequently positive randomised clinical trials have been completed, allowing new consideration of the present therapeutic armamentarium for advanced renal cell carcinoma. These studies were predominantly designed to compare the experimental drugs with 1 of 2 active control arms: interferon alpha-2a or sorafenib. Different from expectations, the final results of some of these studies were not in line with the predictions, and the reasons have not been fully investigated. Consequently, there is a great need for careful analysis of the studies carried out so far, chiefly the role and validity of the control arms. In this regard, the examination of patient baseline characteristics and other factors of potential interest seems fundamental for a correct analysis of the results of these trials and consequent optimal use of the available targeted agents

Piecewise smooth systems near a co-dimension 2 discontinuity manifold: can one say what should happen?

We consider a piecewise smooth system in the neighborhood of a co-dimension 2 discontinuity manifold $\Sigma$. Within the class of Filippov solutions, if $\Sigma$ is attractive, one should expect solution trajectories to slide on $\Sigma$. It is well known, however, that the classical Filippov convexification methodology is ambiguous on $\Sigma$. The situation is further complicated by the possibility that, regardless of how sliding on $\Sigma$ is taking place, during sliding motion a trajectory encounters so-called generic first order exit points, where $\Sigma$ ceases to be attractive. In this work, we attempt to understand what behavior one should expect of a solution trajectory near $\Sigma$ when $\Sigma$ is attractive, what to expect when $\Sigma$ ceases to be attractive (at least, at generic exit points), and finally we also contrast and compare the behavior of some regularizations proposed in the literature. Through analysis and experiments we will confirm some known facts, and provide some important insight: (i) when $\Sigma$ is attractive, a solution trajectory indeed does remain near $\Sigma$, viz. sliding on $\Sigma$ is an appropriate idealization (of course, in general, one cannot predict which sliding vector field should be selected); (ii) when $\Sigma$ loses attractivity (at first order exit conditions), a typical solution trajectory leaves a neighborhood of $\Sigma$; (iii) there is no obvious way to regularize the system so that the regularized trajectory will remain near $\Sigma$ as long as $\Sigma$ is attractive, and so that it will be leaving (a neighborhood of) $\Sigma$ when $\Sigma$ looses attractivity. We reach the above conclusions by considering exclusively the given piecewise smooth system, without superimposing any assumption on what kind of dynamics near $\Sigma$ (or sliding motion on $\Sigma$) should have been taking place.Comment: 19 figure